International Trade and Unemployment: Theory and Cross-National Evidence* Accepted: Journal of International Economics

Pushan Dutt Devashish Mitra† Priya Ranjan INSEAD Syracuse University University of California – Irvine

January, 2009

Abstract We present a model of trade and search-induced unemployment, where trade results from Heckscher-Ohlin (H-O) and/or Ricardian comparative advantage. Using cross-country data on trade policy, unemployment, and various controls, and controlling for endogeneity and measurement-error problems, we find fairly strong and robust evidence for the Ricardian prediction that unemployment and trade openness are negatively related. This effect dominates the positive H-O effect of trade openness on unemployment for capital-abundant countries, which turns negative for labor-abundant countries. Using panel data, we find an unemployment-increasing short-run impact of trade liberalization, followed by an unemployment-reducing effect leading to the new steady state. Keywords: Trade, Search, Unemployment, Ricardian, Heckscher-Ohlin JEL Classification Codes: F11, F13, F16, J64

* We are indebted to the editor, Jonathan Eaton and two anonymous referees for extremely useful comments and suggestions. The standard disclaimer applies. † Corresponding author: Department of Economics, The Maxwell School of Citizenship & Public Affairs, Syracuse University, Eggers Hall, Syracuse, NY 13244, Email: dmitra@maxwell.syr.edu, Ph: (315) 443-6143, FAX: (315) 443-3717.

1 Introduction

While unemployment is one of the big economic problems, trade economists have generally tended to

abstract away from it. Most trade models are full employment models with fully flexible wages. Implicitly,

this means trade economists do not believe that trade is an important factor in the determination of

unemployment. There are, of course, exceptions to this rule, and there does exist a small but growing

literature on the relationship between trade and unemployment.1 Outside the economics profession, there

are people who believe that one of the important effects of trade is the destruction of jobs, leading to

significant unemployment. Such reports are common in the various popular forms of the news media

which completely ignore the creation of new jobs as a result of international trade.2 Therefore, there is

a need for not only theoretical work but also rigorous empirical work investigating the effects of trade on

unemployment.3

In this paper, we present a model of trade and unemployment, in which unemployment is search induced

and trade arises as a result of Ricardian comparative advantage based on relative technological differences

and/or Heckscher-Ohlin comparative advantage based on international differences in relative factor endow-

ments.

Trade affects unemployment through these above sources of comparative advantage in different ways.

If trade is solely driven by Ricardian comparative advantage, then trade liberalization (or tariff reduction)

will result in a reduction in unemployment. However, trade driven by Heckscher-Ohlin based comparative

advantage is expected to reduce unemployment only if the country in question is labor-abundant. Through

this channel, trade liberalization in fact can increase unemployment in a labor-scarce economy. While this

second part of the Heckscher-Ohlin prediction has the potential to be music to the ears of protectionists

1The most prominent contributors to this literature are Carl Davidson and Steve Matusz. See Davidson et al. (1999) for a

representative work and Davidson and Matusz (2004) for a survey. Also see Moore and Ranjan (2005) and Mitra and Ranjan

(2007) for recent contributions to this literature.

2A search of news articles in the New York Times since 1990, reveals a total of 275 articles on NAFTA as the primary

subject. Out of this, 147 articles talk about job destruction in the US as a consequence of NAFTA. Also, Davidson and

Matusz (2004) point out that most of the statements made in the House and the Senate during the NAFTA debate were about

NAFTA’s impact on jobs. They point out, that in sharp contrast, there is not a listing for unemployment in the index of the

4000 pages long Handbook of International Economics, primarily used by academic economists.

3Empirical work on trade and unemployment is virtually non-existent. An exception is some analysis of the correlation

between job destruction and net exports across sectors in chapter 4 of the book by Davidson and Matusz (2004). They find

a negative correlation between the two (equivalent to a positive correlation between net imports and job destruction), and

perform some further regressions to look deeper into this correlation. See Davidson and Matusz (2005) for a more detailed

empirical analysis. For an in-depth and interesting political-economy empirical analysis of how labor turnover in the US

determines support for or against free trade and whether it is along factor lines or industry lines, see Magee, Davidson and

Matusz (2002).

1

in developed countries, our empirical work does not find much support for this prediction. We in fact find

fairly strong and robust empirical support from our cross-country regressions for the Ricardian prediction

that trade openness and unemployment are negatively related across all countries. It is important here

to understand the intuition behind this result. Let us consider a two-sector, single-factor (labor), small-

country Ricardian model. In this setting, trade results in an increase in the value of the marginal product of

labor in one of the sectors (the export sector) due to an increase in the domestic relative price of the good

produced in that sector. Since trade in this set up leads to complete specialization and the other sector

(the import-competing sector), where the value of marginal product of labor would have been lower, cannot

survive trade liberalization, the economywide value of marginal product of labor also goes up. There is

more investment in job search and the posting of jobs and we get a reduction in unemployment.

A similar unemployment reducing effect of trade liberalization is obtained in the theoretical work of

Felbermayr, Prat and Schmerer (2008), where also this effect is productivity driven. However, the increase

in aggregate productivity upon trade liberalization happens through the weeding out of the least productive

firms and the labor reallocation into relatively more productive firms in a model of heterogeneous firm

productivity. The opposite result is obtained in Helpman and Itskhoki (2007) where there is, in addition

to a differentiated-products, heterogeneous-productivity sector with labor-market frictions, a homogenous

good sector with no labor-market frictions (and therefore, no unemployment). To the extent that trade

leads to higher aggregate productivity in the differentiated products sector, this sector expands in size

and the other sector, which has zero unemployment, shrinks. This effect leads to a rise in economywide

unemployment upon liberalizing trade. A similar result is also obtained in Janiak (2007). There are other

papers in the literature, such as Davidson, Martin and Matusz (1999) and Moore and Ranjan (2005) where

the effect of trade on overall unemployment is ambiguous.4

It is also important to understand the intuition behind the alternative Heckscher-Ohlin prediction, even

though it seems empirically not to be the dominant effect of trade on unemployment. When the two tradable

goods here are produced using labor and capital at different factor intensities, before the opening of trade

the relative price of the capital-intensive good is lower in a relatively capital-abundant country than in

the rest of the world. Therefore, opening up to trade will imply an increase in the relative price of the

capital-intensive good in this country. Thus the demand for capital relative to labor goes up. There is

here a modified Stolper-Samuelson effect - the wage falls, the rental on capital rises and the unemployment

rate rises. The results in this model are reversed in a country that is labor-abundant as trade increases the

implicit demand for the services of labor relative to those of capital, which implies that the wage rate goes

up and unemployment goes down.

4 In Davidson, Martin and Matusz the results depend on intersectoral and international differences in labor-market frictions.

In Moore and Ranjan, there are two kinds of labor (skilled and unskilled) and the effects of trade on the unemployment of

these two kinds of workers go in opposite directions, making the impact on overall unemployment ambiguous.

2

We also discuss comparative advantage arising out of differences in intersectoral asymmetries in search

frictions and arising from the interaction of international differences in search efficiency and intersectoral

differences in search intensity. Some of the predictions here on unemployment can look like the pure

Heckscher-Ohlin ones. However, it is not possible to empirically investigate most of the predictions based

on search friction asymmetries due to serious data limitations.

In our theory section, we also discuss the short-run impact effect of trade liberalization. This discussion

is guided by a model of endogenous job destruction. Starting from a steady state in which the job creation

rate equals the job destruction rate and not allowing intersectoral labor mobility, upon trade liberalization

we get net job creation in the sector with a price rise (export sector) and net job destruction in the sector

that experiences a price fall (import-competing sector). Job creation takes time, while job destruction can

take place immediately. Therefore, the impact effect will be an increase in unemployment in the sector

with the price fall and no change in unemployment on impact in the sector with the price rise (even though

unemployment gradually falls there on its way to a new steady state). Thus, for the economy as a whole,

starting from a steady state, unemployment rises at the point of impact.

Our empirical work in this paper uses cross-country data. We use a number of variables that capture

trade policy such as unweighted average tariffs, import weighted average import duty, the overall trade

restrictiveness index (OTRI), a measure that includes both formal and informal barriers to trade and

the standard measure of openness measured as the ratio of trade to GDP. The dependent variable is

overall national unemployment rate. We use several control variables that capture the varying labor-market

institutions across countries. These capture the strength of labor unions, labor-market rigidity, the nature

of labor laws etc. Other controls capture the varying macroeconomic economic environments of the different

countries. These controls include variables such as output volatility, black-market premium etc. We also

check robustness of results to instrumenting our trade policy variables. The reasons for instrumenting arise

from the endogeneity of trade policy to unemployment as well as possible measurement errors in trade

policy variables. The instruments we use include the number of years as a GATT/WTO member since the

GATT was founded and a lagged variable to capture reliance on tax revenues from domestic sources. For

the traditional openness measure, we use gravity-based variables and additional geographical variables as

instruments. In these cross-country regressions, we are investigating steady-state theoretical predictions,

and so we primarily use decade averages of data to maximize coverage.

We find fairly strong evidence for the Ricardian prediction that unemployment and trade openness

are negatively related (protection and unemployment are positively related). Our results confirming this

prediction are robust to the inclusion and exclusion of controls, and hold using both ordinary least squares

as well as instrumental-variables approaches. We find only weak support for the alternative H-O prediction

that the relation between trade and unemployment changes from negative to positive as we move from labor-

3

abundant to capital-abundant countries. These results, by no means, imply the absence of Heckscher-Ohlin

type effects that occur through changes in the sectoral composition of the economy. Rather, it is quite likely

that such effects, when they go in the opposite direction, get dominated by Ricardian-type productivity-

related effects of trade on unemployment. This seems to us to be the most plausible interpretation of our

empirical results.

We also use panel data to look at the short-run impact effect of trade liberalization. As predicted by

theory that endogenizes job destruction, we find that trade liberalization leads to an immediate short-term

increase in unemployment. Unemployment, however, starts to decline after a lag, and our estimates imply

an overall long-run decline in unemployment.

The plan of the rest of the paper is as follows. We first present a unified model from which we derive

as special cases, the Ricardian and Heckscher-Ohlin implications of trade liberalization for unemployment.

We also derive predictions based on comparative advantage driven by asymmetries in search frictions. We

then derive predictions regarding the impact effect of trade from a model of endogenous job destruction.

Having derived empirical predictions from the theoretical model, we undertake our empirical analysis.

2 Theory

2.1 Production Structure

Consider an economy that produces a single final good and two intermediate goods. The final good is

non-tradable, while the intermediate goods are tradable. The final good is denoted by Z and the two

intermediate goods are denoted by X and Y. Further, denote the prices of X and Y in terms of the final

good as px and py, respectively. The production function for the final good is as follows:

Z =AX1−αY α

αα(1− α)1−α; 0 < α < 1

Given the prices px, and py, of inputs, the unit cost for producing Z is given as follows.

c(px, py) =1

A(px)

1−α (py)α (1)

Since Z is chosen as the numeraire, c(px, py) = 1, or

1

A(px)

1−α(py)

α= 1 (2)

The production function for Z implies the following relative demand for the two intermediate goods.

Xd

Y d=(1− α)py

αpx(3)

Labor and capital are the two factors of production. The total number of workers in the economy is L, each

supplying one unit of labor inelastically when employed. The amount of capital is K. Our description of the

4

labor market corresponds to a standard Pissarides (2000) style search model embedded in a two sector set

up. A producing unit in the intermediate goods production is a job-worker match. New producing pairs

are created at a rate determined by a matching function of two measures of labor market participation,

vacancies and unemployment. Job destruction is a response to idiosyncratic shocks to the productivity of

existing job-worker matches.

Once a match is created, a firm rents capital and undertakes production. For simplicity, assume that

firms can return the capital to the owner upon the destruction of a job. The production functions (in these

one-worker firms) in the two intermediate goods sectors, once the matches are formed, are given by

x = hxkφxx ; y = hyk

φyy ; 0 ≤ φi ≤ 1

where ki is the amount of capital used in a representative single-worker firm in sector i = x, y, while φi is

a parameter capturing the capital intensity of sector i’s Cobb-Douglas production function and hi is the

sector’s technology parameter, capturing its total factor productivity. If Li is the total number of workers

affiliated with sector-i, ui the unemployment rate in sector i, then the number of employed in sector i is

(1− ui)Li. The aggregate production in each sector is given by

X = hx(1− ux)Lxkφxx ;Y = hy(1− uy)Lyk

φyy

and the amount of capital employed in each sector is given by

Kx = kx(1− ux)Lx;Ky = ky(1− uy)Ly

Note that the composition of the economy’s labor force, L into Lx and Ly is endogenously determined in

our model (as explained later). The following, therefore, represents our binding labor constraint:

Lx + Ly = L (4)

The market clearing condition in the capital market implies the following.

(1− ux)Lxkx + (1− uy)Lyky = K (5)

The relative supply of the two intermediate goods is

XS

Y S=

hx(1− ux)Lxkφxx

hy(1− uy)Lykφyy

(6)

The total number of matches in the labor market is determined by the matching technology given as

follows. Let vi be the vacancy rate (i.e., the number of vacancies divided by the labor force) in sector-i.

Define θi ≡ viuias a measure of market tightness, and let mi be a scale parameter in the matching function.

Then, write the flow of matches in each sector per unit time as follows:

Mi(viLi, uiLi) = mivγi u

1−γi Li = miθ

γi uiLi; 0 < γ < 1 (7)

5

where γ is a parameter capturing the vacancy intensity of this Cobb-Douglas matching function. Then, the

exit rate (from unemployment) for an unemployed searcher in sector i is Mi

uiLi= miθ

γi , and the rate at which

vacant jobs are filled is Mi

viLi= miθ

γ−1i . The former is an increasing function of market tightness, and the

latter is a decreasing function of market tightness.

Assume that the matches in sector-i are broken at an exogenous rate of λi per period. λi can be viewed

as an arrival rate of a shock that leads to job destruction. Given the above description of labor market, the

net flow into unemployment per period of time is

.ui = λi(1− ui)−miθ

γi ui

In the steady-state the rate of unemployment is constant. Therefore, the steady-state unemployment in

sector-i is given by

ui =λi

λi +miθγi

(8)

Denote the recruitment cost in sector-i in terms of the final good by δi, and the exogenous discount

factor by ρ. The asset value of a vacant job, Vi, is characterized by the following Bellman equation

ρVi = −δi +miθγ−1i (Ji − Vi) (9)

where Ji is the value of an occupied job.

Free entry in job creation implies Vi = 0, which implies the following from (9)

Ji =δi

miθγ−1i

; for i = X,Y (10)

Denoting the wage of workers in sector-i by wi in terms of the numeraire good and the rental of capital by

r, the asset value of an occupied job, Ji, satisfies the following Bellman equation

ρJi = pihikφii − rki − wi − λiJi (11)

Making use of (11) to substitute out Ji from equation (10) we get the following equation

pihikφii − rki − wi =

(ρ+ λi)δi

miθγ−1i

(12)

(12) is another way to write the zero profit condition from a vacant job mentioned earlier.

The optimal choice of ki is determined by maximizing Ji in equation (11) taking the wage rate wi and

the rental r as given. This leads to the following condition governing the optimal choice of ki

pihiφikφi−1i = r (13)

6

2.2 Wage Determination

On the worker side, unemployed workers in each sector receive a flow benefit of b in units of the final good.

This “benefit” includes the value of leisure as well as unemployment insurance payments. LetWi denote the

present discounted value of employment in sector i and Ui the present discounted value of unemployment.

The Bellman equations governing Wi and Ui are given by:

ρWi = wi + λi(Ui −Wi) (14)

ρUi = b+miθγi (Wi − Ui) (15)

Wage is determined through a process of Nash bargaining between the worker and the entrepreneur

where the value of a job for an entrepreneur is given by Ji in (11) and the surplus of a worker from a job is

Wi − Ui. Denoting the bargaining power of workers by β, Nash bargaining implies the following equation

for wages.

Wi − Ui =β

1− βJi (16)

Note also from (15) and (16) that

ρUi = b+β

1− βmiθ

γi Ji = b+

β

1− βδiθi (17)

where the last equality is obtained by using (10).

By substituting out Ji, Wi, and ρUi, using (11), (14), and (17), respectively, in (16), we can derive the

following expression for wage:

wi = (1− β)b+ β(pihikφii − rki + δiθi) (18)

Now, a worker should be indifferent between searching in either sector. Therefore, the no arbitrage

condition is given by

Ux = Uy

which in turn implies from (17) that in equilibrium

δxθx = δyθy (19)

That is, the market tightness in each sector is proportional to the recruitment cost.

The model is solved as follows. Start with any pxpy. The absolute prices px and py corresponding to this

pxpyare obtained from (2). For this pair of prices px and py the following 11 variables-wi, r, θi, ui, Li, ki can

be found from the equations (4), (5),(8), (12), (13), (18), (19), all derived above.

Next, we make the following assumption and discuss the implications of relaxing it later.

7

Assumption 1: The search parameters are symmetric across the two sectors, i.e., mx = my, δx =

δy, λx = λy

From the (19) we get θx = θy. From (12) and (18) we get

wi = b+β

1− β(δiθi +

(ρ+ λi)δi

miθγ−1i

) (20)

Therefore, under assumption 1 above we get wx = wy.

It can also be verified that for a given product price ratio, a higher b, β or δ leads to a lower θ and

consequently a higher unemployment. In the empirical exercise we will use proxies for b and β as controls.

2.3 The Case of Ricardian Comparative Advantage

In addition to assumption 1, we make the following assumption on factor intensities:

Assumption 2: The capital intensities are the same in both sectors, i.e., 1 > φx = φy = φ ≥ 0.Under assumption 2 the two production functions differ only by a constant productivity term hi. In this

sense, the case we are analyzing is Ricardian.5

Since wx = wy, it implies from (12) and (13) that under assumption 2

(1− φ)pxhxkφx = (1− φ)pyhyk

φy

Upon substituting the expression for ki from (13) in the above we get the result that the relative price must

satisfy the following.pxpy=

hyhx

(21)

That is, under the identical factor intensity assumption, the relative price is determined by purely exogenous

productivity differences between the two sectors.

2.3.1 International Trade

Suppose this economy is now opened up to international trade. Also, suppose this economy has a com-

parative advantage in X and is a small open economy. Therefore, the autarky price of X is less than

its world price. Using the A superscript to denote autarky and W superscript to denote world, we have³pxpy

´A=

hyhx

<³pxpy

´W. After opening up to trade the relative price of X will rise to the world relative

price and hence hxpx > hypy. This implies from (13) that ky < kx and hence hypykφy < hxpxkφx . Since the

vacancy costs and the bargaining power of workers are the same across the two sectors, Nash bargaining

implies that the same wage rate will have to be paid in the two sectors if both sectors exist. However,

5Clearly, φx = φy = 0 is a special case of the model of Ricardian comparative advantage described above, and that is the

case where only labor gets used in the production of the two goods and thus, capital is no longer a productive factor. Thus,

this boils down to a traditional one-factor Ricardian model and all the results derived for φx = φy ≥ 0 will hold.

8

hypykφy < wx +

(ρ+λ)δmθγ−1

= hxpxkφx , and hence sector Y is no longer viable in the economy. Therefore, as in

the standard Ricardian model, the economy will specialize in the production of X.

What happens to unemployment after opening to trade? An increase in³pxpy

´implies from (2) an

increase in px and a reduction in py. An increase in px implies increased profitability of jobs in the X

sector at autarky wx and θ. On the other hand, there is a reduction in the profitability of jobs in the Y

sector. This leads to more job creation in the X sector and a reduction in job creation in the Y sector.

Thus the wage and market tightness are pushed up in the X sector and they are pushed down in the Y

sector, which makes X sector more attractive for workers. An increase in px also increases the value of the

marginal product of capital in the X sector (and reduces the value of the marginal product of capital in

the Y sector) which leads to a movement of capital away from the Y sector into X sector. The consequent

increased capital per worker in the X sector further increases the value of a job there leading to further job

creation. The process stops once all capital and labor have moved to the X sector. In an online appendix

(http://faculty.insead.edu/dutt/unemp) to this paper, we verify that, starting from the autarky price, as

px increases, θ increases, which in conjunction with (20) and (8), leads to an increase in w and a decrease

in u. Thus, movement from autarky to free trade reduces the unemployment rate and increases the wage

rate.

Starting from a positive tariff on good Y , trade liberalization (reduction in the tariff) has a similar

effect. Even though there is complete specialization in this model (in the presence of a tariff that is not

totally prohibitive), unemployment responds to tariff here. A reduction in tariff leads to an increase in the

domestic price of X and therefore an increase in w and a reduction in the unemployment rate.

Thus we have the following result:

Proposition 1 Opening up to trade or a reduction in import tariffs in a two-sector model of Ricardian

comparative advantage with search-generated unemployment (and with symmetric labor-market frictions

across the two sectors) leads to a decrease in unemployment and an increase in the real wage of workers.

A similar result can be derived in a Ricardian model with a continuum of goods.

2.4 Comparative Advantage Based on Factor Proportions (Heckscher-Ohlin)

In place of Assumption 2, we now make the following strong factor intensity assumption.

Assumption 3: φx > φy > 0

We continue to make assumption 1 of identical search parameters across sectors. We can easily show

that the relative supply of good X is increasing in the relative price pxpy. Using (12), (13), (18), and (20) we

9

get

kx =

µφyφx

¶ φyφx−φy

µ1− φx1− φy

¶ φy−1φx−φy

µhxpxhypy

¶ 1φy−φx

(22)

ky =

µφyφx

¶ φxφx−φy

µ1− φx1− φy

¶ φx−1φx−φy

µhxpxhypy

¶ 1φy−φx

(23)

Therefore, an increase in pxpydecreases both kx and ky. In addition, an increase in

pxpy, from (2), implies an

increase in px and a decrease in py. From (13), thus we have an increase in r. Further, since pyhykφyy −rky =

(1 − φy)pyhykφyy and both py and ky decrease, pyhyk

φyy − rky decreases as well. Next, note that identical

wages (due to identical θ from identical δ) in the two sectors imply

pyhykφyy − rky = pxhxk

φxx − rkx (24)

Since the l.h.s of (24) decreases, it implies a decrease in r.h.s. as well. Now, it can be easily shown using

(12) and (18) that this leads to a decrease in w and θ, and consequently an increase in u. Intuitively, when

px increases and py decreases, in order for sector Y to remain viable, the labor cost must decrease because

sector Y is relatively labor abundant. This is accomplished by a decrease in w and θ.

Since the two sectors have identical θ, the sectoral unemployment rates are identical as well, therefore,

the factor market clearing condition (5) can be written as

εkx + (1− ε)ky =K

(1− u)L(25)

where ε = LxL is the share of sector X in the total labor force, and Lx is the amount of labor affiliated

with sector X in steady state. With this notation, we can write the relative supply of the two intermediate

goods X and Y asXs

Y s=

εhxkφxx

(1− ε)hykφyy

(26)

In the online appendix (http://faculty.insead.edu/dutt/unemp), we have proved that Xs

Y s is increasing in

p ≡ pxpy. From (3) we know that Xd

Y d is decreasing in p. Therefore, the autarky equilibrium is obtained by

the intersection of the relative demand and the relative supply curves.

What happens whenK

Lincreases? We have shown above that u, kx and ky are fully determined by p.

Thus, holding p constant implies setting dkx = dky = du = 0. Holding p constant, from equation (25) we

have

(kx − ky)dε =1

(1− u)d

µK

L

¶(27)

Holding the relative price p constant, the only impact of an increase inK

Lis to increase ε, which in turn

implies a rightward shift in the relative supply curve. Therefore, the equilibrium relative price p is lower the

larger theK

L. Therefore,

K

Lbecomes a source of comparative advantage as in the standard Heckscher-Ohlin

model.

10

2.4.1 Impact of Trade

Assuming that our economy is capital-abundant relative to rest of the world, before the opening of trade in

the intermediate goods, the comparative statics discussed earlier implies that the relative price of the capital

intensive intermediate good X is lower in our economy. Therefore, opening up to trade in the intermediate

goods will imply an increase in the relative price of X. So, the impact of trade in intermediate goods is

captured by an increase in the relative price pxpy. This implies an increase in px and a decrease in py. As

discussed earlier, this leads to the following changes: dw < 0, dr > 0, and dθ < 0, du > 0. The opposite

happens to a country having a comparative advantage in the labor intensive good. Intuitively, even though

job creation becomes more attractive in the X sector upon impact, the consequent increase in the rental of

capital reduces the value of a job in both sectors leading to increased unemployment and lower wages in a

capital abundant country. The results are summarized in proposition 2 below.

Proposition 2 The impact of international trade (or an import tariff reduction) on a capital abundant

country (when search parameters across sectors are the same but factor intensity parameters different) is

a decrease in the wage rate and an increase in the rate of unemployment of labor, while in the case of a

labor-abundant country the wage rate goes up and unemployment goes down as a result of trade liberalization.

Why do we get different results in the Ricardian and Heckscher-Ohlin cases? Let us first look at the

Ricardian case (where both sectors have the same factor intensity or alternatively where there is only a single

factor). Opening to trade here destroys the viability of the sector in which the country has a comparative

disadvantage. In addition, the price of the other good (the one produced in the comparative-advantage

sector) goes up. Hence there is an increase in the value of a job and consequently, a reduction in steady-state

unemployment. On the other hand, in the two-factor Heckscher-Ohlin world (which is a world where the

two sectors have different factor intensities), the reaction to opening to trade is somewhat more complex. In

a capital-abundant country, there is an expansion of the capital-intensive export (comparative-advantage)

sector accompanied by a shrinking of the labor-intensive import-competing (comparative-disadvantage)

sector. This leads to increased economywide demand for capital relative to labor. The consequent increase

in the rental price of capital has a negative effect on the value of a job in both sectors. The net effect

is increased unemployment and lower wages in a capital abundant country. The reverse is the case in a

labor-abundant country.

To sum up, under assumption 1 on identical search parameters in the two sectors we derive unambiguous

implications of trade for unemployment in the two classic models of trade: the Ricardian model and the

Heckscher-Ohlin model. Since in our empirical exercise we are unable to control for search parameters, this

was the appropriate theoretical framework to present. However, the unambiguous theoretical predictions as

well as the strict separation between Ricardian and Heckscher-Ohlin determinants of comparative advantage

11

will not hold if search parameters differ across sectors and countries6 . Davidson, Martin, and Matusz (1999)

provide a rich model to study the impact of trade on unemployment in several different scenarios depending

on whether search parameters differ across sectors and/or countries.

2.5 The Short-Run Impact Effect of Trade Policy

So far our theoretical exercise has focused on the steady state effects of trade on unemployment. To

analyze the instantaneous impact effect of a change in trade policy assume no intersectoral mobility of

labor and endogenize job destruction along the lines of Pissarides (Chapter 2). A firm-job pair starts at

full productivity at the point of creation. At each subsequent point in time, a firm-specific (job-specific)

productivity shock is received by each firm at a Poisson arrival rate. The threshold productivity level for a

firm’s survival is determined by profits corresponding to the productivity level and is the one for which the

firm just breaks even. Any shock that lowers the productivity level below this leads to the destruction of

the firm or job. With a change in trade policy (increase or reduction in import protection), the domestic

relative price of one of the two sectors goes up, while that of the other sector goes down. In the sector where

the price goes up, the threshold productivity for survival falls, as a result of which the job destruction rate

falls, and market tightness and job creation rate rise. In the other sector where the price goes down, the

threshold productivity rises as a result of which the job destruction rate rises and the job creation rate falls.

Starting from a steady state in which the job creation rate equals the job destruction rate, upon a change

in trade policy, we get net job creation in the sector with a price rise and net job destruction in the sector

that experiences a price fall. Job creation takes time, while job destruction can take place immediately.

6For a detailed discussion of introducing asymmetries in the various labor market and search parameters, we refer the

interested reader to our online appendix (http://faculty.insead.edu/dutt/unemp). For example, in our baseline Ricardian

model (φx = φy > 0), we know that the capital intensity is the same in the two sectors if they both face the same factor

costs. However, if under such conditions, we introduce search cost asymmetries across sectors, we will get different costs per

unit of labor in the two sectors. Wage plus search cost per worker will be higher in the sector with the higher vacancy cost,

and so the relative factor costs (inclusive of search costs) faced will be different in the two sectors. Thus, ex post the capital

intensities in the two sectors will be different. Ex post, the sector with the higher search cost will be more capital intensive. If

the sectoral search costs are the same across countries (within a sector), a capital abundant country will have a comparative

advantage in the sector with the higher vacancy costs. When such a country opens up to trade, the domestic price of good

produced in the higher vacancy cost (ex post capital-intensive) sector goes up and that in the other sector goes down. Thus,

due to the expansion of the capital-intensive sector (and a contraction of the labor-intensive sector) we should expect to have

a reduction in the economy’s overall demand for labor and therefore, a reduction in labor market tightness and an increase

in unemployment in each of the two sectors. Also, as mentioned above, the share of the ex post capital intensive (the higher

unemployment) sector goes up. Thus, we should expect an increase in unemployment. By symmetric logic, unemployment

in a labor-abundant country should go down when it opens up to trade. Thus we have a composition effect that goes very

much in the same direction as the Heckscher-Ohlin prediction in proposition 2 even though the two sectors had identical factor

intensities ex ante.

12

Therefore, the impact effect will be an increase in unemployment in the sector with the price fall (and in the

overall economy) and no change in unemployment on impact in the sector with the price rise (even though

unemployment gradually falls there on its way to a new steady state), irrespective of whether the capital

intensities are the same or different in the two sectors.

2.6 The Role of Labor Force Participation

In our analysis, we have so far abstracted from labor force participation. As is discussed in the online

appendix (http://faculty.insead.edu/dutt/unemp), this is not a problem if the matching function exhibits

constant returns to scale (which has been found to be empirically valid) as is assumed in the theoretical

model. Constant returns in matching makes unemployment independent of the size of the labor force. Even

if the matching function exhibits non constant returns to scale, not controlling for labor force participation

(as long as one is controlling for working age population) is not a problem. The labor force participation

rate is then a function of the working age population. Plugging this function into a function determining

unemployment gives us a reduced form function for the unemployment rate that has working age population

as an argument but not labor force participation rate. Only if we really want to know the effect of trade

policy on the unemployment rate specifically for given participation rates, do we control for and instrument

the labor force participation rate.

3 Data Description

To examine the relationship between trade protection and unemployment we collected data on multiple trade

policy measures, unemployment rates and a variety of controls over the period 1990-2000. Details on the vari-

ables used, along with additional results are available in an online appendix (http://faculty.insead.edu/dutt/unemp).

We use the 1990s’ average for our cross-sectional analysis and the time series for each country for the period

1985-2004 for our panel analysis.

3.1 Unemployment rate

Our dependent variable is the unemployment rate (as percentage of the labor force) from the International

Finance Statistics. For our cross-sectional analysis, this variable is averaged over the decade of the 1990s

to smooth out any business cycle fluctuations. We have data on 92 countries and the variable ranges from

a low of 0.9% for Azerbaijan to a high of 55.6% for Ethiopia.7

7 In our sample, Ethiopia drops out due to missing data on labor market institutions.

13

3.2 Trade Policies

Countries resort to a variety of policy instruments in order to protect trade. These include: tariffs, quotas,

non-automatic licensing, antidumping duties, countervailing duties, tariff-rate quotas, export taxes, etc.

Finding a single measure of trade protection that summarizes such a multiplicity of instruments is a task

economists have long struggled with. Since it is impossible to capture and summarize the wide variety of

trade policy instruments used, we use the following policy measures in addition to an outcome measure,¡X+MGDP

¢.8 Our first direct measure is the unweighted average external tariff data recently made available

by the World Bank. A second measure is total import duties collected as a percentage of total imports

from the World Development Indicators (also averaged for the 1990s).9’10 Given that tariffs are only one of

many direct measures of trade protection, Kee, Nicita and Olarreaga (2006) have attempted to combine the

myriad trade policy instruments into an Overall Trade Restrictiveness Index (OTRI). It is the equivalent

uniform tariff that would keep imports of a country at their observed levels, and it also takes into account

the ad-valorem equivalent of non-tariff barriers. The construction methodology follows that of Anderson and

Neary (1994). Finally, due to the presence and importance of informal trade barriers in the determination

of trade patterns and volumes (See Anderson and Marcouiller, 2002), we use a measure from the Economic

Freedom of the World Project. This measure is based on a survey question from the Global Competitiveness

Report that asks respondents to rate hidden import barriers. Countries receive a rating from 0-10 with

higher numbers indicating lower barriers. We combine this rating with a similar rating on formal trade

barriers (tariffs and trade tax revenues) using a simple average. Thus our modified GCR measure is a

combination of both informal/hidden and formal barriers. In addition, it is recoded as 10 minus original

rating to make our modified GCR measure increasing in the size of the trade barriers. This variable is

available for a single year in the 1990s.11

8 X+MGDP

, while capturing the effects of many different trade related policies, can also capture the effects of policies and

structural characteristics, unrelated to trade policy. These could include differences in tastes, macroeconomic shocks and

policies, geographic attributes, and other factors such as rainfall.

9Note the import duty measure is a weighted average of import duties on each good where the weights are the share of

imports of that good in total imports.

10While neither the average tariff measure nor the import duty measure is perfect due to the numerous problems arising from

their highly aggregate nature, Rodriguez and Rodrik (2000) argue that these are the most direct measures of trade restrictions,

with little evidence for the existence of serious biases, and that they do a relatively decent job in ranking countries according

to the restrictiveness of their trade regimes.

11We also examined quota coverage ratio as a measure of protection. However, this variable is available for only 28 countries

for a single year in the 1990s. Results with this measure are available in the online appendix.

14

3.3 Controls

Botero et al (2004) argue that every country in the world has established a complex system of laws and

institutions intended to protect the interests of workers and to help assure a minimum standard of living

for its population. These include employment laws that govern the individual employment contract, and

collective or industrial relations laws that regulate the bargaining, adoption, and enforcement of collective

agreements, the organization of trade unions, and the industrial action by workers and employers. We

use their index of employment laws that captures the rigidity of alternative employment contracts, cost of

increasing hours worked, cost of firing workers, and difficulty of dismissal procedures. The index measures

both the legal and effective protection available to workers. Our second control is an index of labor union

power again from Botero et al (2004). This variable measures protection afforded to and the power of labor

unions. Controlling for labor union power and employment laws is critical since this could lead to an

omitted variable bias. Countries with strong union power are likely to exhibit both higher levels of trade

protection and higher rates of unemployment.12 Both measures are available for a single year (1997). A

third institutional control is the civil rights index from Freedom House. Measured on a scale of 1 to 7, with

higher numbers signifying greater restrictions on civil liberties, this variable captures freedom of expression,

the right to organize, the rule of law and personal autonomy.

To control for the effect of recessions and expansions, booms and busts, financial crises, output collapse

in former communist Eastern European states etc., we include a measure of output volatility. We follow

Ramey and Ramey (1995) and measure output volatility as the standard deviation of the annual growth

rate of GDP per capita for each of the countries in our sample over the period 1990-2000. We also included

the Black Market Premium on the exchange rate to capture macroeconomic distortions. We control for

the size of the economy using the working-age population in the ages 15-6413 and real GDP from World

Development Indicators. The two controls together would also effectively control for GDP per capita, which

is a measure of the level of development . Finally, as a robustness check we add measures of labor market

participation - both the total participation rate (percentage of total population between the ages 15-64

who are economically active) and female labor market participation rate (economically active females as

a percentage of the female population of ages 15-64). Both variables are from the World Development

Indicators.

Tables 1 provides the summary statistics of all our variables.

12Within a Heckscher-Ohlin framework, strong union power is likely to result in higher protection and higher unemployment

for capital-abundant countries only. In labor-abundant countries, a stronger lobbying presence by workers should result in

lower protection. We evaluated this by including an interaction between labor union power and K/L in H-O model. The

interaction variable however, proved to be insignificant. We also used data on trade union membership as a percentage of

labor force from Rama and Artecona (2002). This variable was not significant in any of the specifications.

13Our measure is the working-age population in a country between the ages 15-64.

15

4 Trade Protection and Unemployment: Results

4.1 Ricardian Specification

Proposition 1 states that within a model of Ricardian comparative advantage a higher tariff should lead

to higher levels of unemployment. We evaluate this proposition by regressing the unemployment rate on

each of our 6 measures of trade policy. Our objective is to examine whether more protectionist countries

experience higher rates of unemployment, and whether this relationship is robust to controlling for labor

laws, macroeconomic distortions and country size, and to endogeneity concerns, and that the relationship

holds both across countries and within countries over time.

Figure 1 (presented right above Table 2) shows the correlation between unemployment rate and the

unweighted tariff measure.14 There is a clear positive relationship between unemployment and tariffs. The

unconditional raw correlation is positive and significant at the 1% level of significance.

4.1.1 OLS Estimates

Tables 2 and 3 check the robustness of this relationship to alternate measures of protection and to the

inclusion of controls. Table 2 shows that almost all our measures of trade protection, are positively and

significantly associated with higher rates of unemployment.15 All models as a whole are significant and

our variables account for 4-24% of the cross-country variation in unemployment rates. Table 3 shows that

this relationship is robust to controlling for country size, for labor union power, for employment laws, for

macroeconomic distortions and fluctuations, and civil liberties. Here our variables account for 20-49% of

the cross-country variation in unemployment rates. In terms of the controls, we find evidence that countries

where labor unions have greater power also exhibit higher rates of unemployment. This is consistent with

the outcome of our comparative static exercise with respect to b and β alluded to in the theory section.16

The numbers in table 3 imply that a 1% increase in the average tariff rate increases the unemployment

rate by about 0.35%. Across protection measures, the unweighted tariff rate has the strongest effect — a

one standard deviation increase in leads to a 2.4% increase in the unemployment rate. The correspond-

ing numbers for import duties, OTRI, GCR, and openness measures are 2.3%, 1.7%, 2.1% and -1.2%,

respectively.

14Similar graphs using alternate protection measures are available in the online appendix.

15Note that X+MGDP

is a measure of openness so we expect a negative sign on this variable.

16 If we use the quota measure, it has the right sign, but is not significant. Partly this is due to the limited data on this

measure and partly because the variable is subject to measurement error. The coverage ratio only suggests that barriers to

trade exist, but cannot measure their effect (see Harrigan, 1993.) In fact, once we instrument quotas, we obtain a positive and

significant coefficient, suggesting attenuation bias.

16

4.1.2 More Controls and Instrumental Variable Estimates

There are two potential problems with the results reported in table 3: omitted variables and endogeneity of

trade policies. It is plausible that an omitted variable may affect both unemployment and trade policies. For

instance, countries that follow good macroeconomic policies may also be less protectionist. While we have

two controls for macroeconomic distortions, output volatility and the black market premium on the exchange

rate, the omitted variable critique can still be made. Second, countries that exhibit higher unemployment

may face populist pressures (domestically) to raise trade barriers (reverse causality). Indeed, this version

of the reverse causality argument does generate a positive (conditional) correlation between unemployment

and protection.

First, we experimented with a host of other macroeconomic policies as controls. Since monetary policy is

frequently used as a tool to manage the trade-offs between inflation and unemployment, we added controls

for nominal interest rate, and inflation rates. We also controlled for deterioration in the terms of trade

(between the decade of the 80s and 90s), and used a measure of domestic distortions from Alesina and

Perotti (1996). Our results survive the use of these additional regressors. For instance, with tariffs, we

obtain

unemployment ratei = 0.348∗∗∗(0.146)

tariff i + controls

R2 = 0.34, N = 46

where *** indicates significance at the 1% level. Other trade policy measures yield results similar to those

in table 3.17

Next, we address endogeneity concerns by using instrumental variables.18 Incidentally, instrumental

variables will help us also deal with measurement error problems, which might be present in many of the

protectionist measures. The presence of measurement error creates an attenuation bias, i.e., it works against

finding a significant relationship between protectionism and unemployment. If the instruments help us deal

with the measurement error we should see an increase in the absolute value of the coefficient. If, on the

other hand, endogeneity has an important impact on our OLS estimates, then we should see a decrease in

the absolute value of the coefficient on trade policy.

Finding good instruments for trade policy is not a simple task. The instruments for trade policies we

propose are the number of years that a country has remained outside of the GATT/WTO since its inception

17Please see the online appendix.

18A second way to deal with reverse causality is to use lagged values of the trade protection measures. When we regress

unemployment in the 1990s on trade policies in the 1980s all the results on the unweighted tariff, import duties and X+MGDP

are confirmed. While the coefficients are positive and significant for the protection measures (unweighted tariff and import

duties) and negative and significant for the openness measure, X+MGDP

, the magnitude of the effect declines, as compared to

those in table 3.

17

in 1948, and the proportion of tax revenues that each country obtained from taxes on domestic activities

in the 1980s. Rose (2004) finds that nearly all countries liberalized after acceding to GATT, but not

immediately. The average lag between GATT accession and liberalization of the trade regime is almost a

decade. So the longer a country stays outside the GATT the larger will be its degree of protectionism. The

rationale for the other instrument is as follows: Rodrik (1995) argues that developed countries have advanced

tax structures and are less likely to rely on trade taxes as a source of revenue. The high administrative

cost of raising revenue from domestic taxes is an important reason for the use of tariffs in the early stages

of development (Limão and Panagariya, 2007). To capture this revenue motive for protection, we therefore

include the share of tax revenues from domestic sources in overall tax revenues for the decade of the 80s

(lagged value of dependence on domestic tax revenues).19

For the outcome based measure of trade policy¡X+MGDP

¢, we use a distinct set of instruments. The

instruments we use are those suggested by Frankel and Romer (1999) and Rose (2004). Frankel and Romer

regress bilateral trade flows (as a share of a country’s GDP) on measures of country mass, distance between

the trade partners, and a few other geographical variables, and then construct a predicted aggregate trade

share for each country on the basis of the coefficients estimated. This constructed trade share is then used

as an instrument for actual trade shares. Rose (2004) calculates a remoteness index as a weighted average

of a country’s trading partners’ GDP where the weights are distance to the trading partners. This is a

multilateral analogue to geographic distance and affects trade volumes. Our first stage regressions support

these conjectures across all measures and yield partial R2s between 18% and 57% (see second-from-last row

of table 4).

Table 4 shows the IV estimates. The coefficient on all protectionist measures, apart from OTRI, remains

positive and significant, while that on¡X+MGDP

¢remains negative and significant. However, the number of

observations for OTRI is small, so that inferences based on this measure are less likely to be valid. For a

majority of the measures of trade policies, we observe only a marginal increase in the absolute value of the

estimates (when we restrict our OLS estimates to the same set of countries as those in table 4.) In fact

across measures of trade policy, the OLS estimates from table 3 are within the 95% confidence interval of

IV coefficient estimates in table 4.

Next, we provide purely economic arguments as to why the two instruments we use are good instruments.

To be valid instruments, neither should be correlated with unemployment directly in addition to being

19Two other instruments we experimented with are a developing country dummy and a variable from The World Values

Survey which reports the proportion of respondents in each country who feel that imports should be limited. If governments

respond to populist pressures this should also be a valid instrument for trade policies. The developing country dummy helps

capture the fact that developing country members of the GATT often get concessions with respect to showing reciprocity to

trade reforms by other member countries. The coefficient on trade policies remain positive and significant if we use either or

both these measures as additional instruments. All first stage regressions are available from the authors on request.

18

related to it through our right-hand side variables, i.e., the instrumental variables should not be correlated

with the error terms of our regressions. First, the number of years outside the GATT/WTO will affect

unemployment only through the tariff level, since negotiations on trade policies are the primary function

of GATT/WTO.20 Also this variable is not expected to be endogenous to unemployment. Second, the

lagged value of the dependence of a country on domestic sources of revenues should not have any additional

relationship with unemployment above and beyond the one through tariffs and beyond what is captured

by a country’s income level. Finally, the gravity and additional geographical variables used to instrument

openness are completely exogenous to our model and should not be correlated with unemployment above

and beyond their correlation through our right-hand side variables. We also provide some econometric

justifications for the validity of our instruments. Hansen-Sargan tests (the p-value is reported on the last

row of table 4) fail to reject the null hypothesis of overidentifying restrictions confirming the validity of our

instruments. The only exception is the OTRI measure where again the small sample size comes in the way

of making valid inferences. Moreover, for all estimates, apart from OTRI, the conditional likelihood ratio

statistic of Moreira, the Anderson-Rubin test, and the Stock-Wright S-statistic reject the null hypothesis

that the coefficient of protection in the structural equation (unemployment as a function of protection and

controls) equals zero. These tests are robust to the presence of weak instruments (Stock, Wright and Yogo,

2002.)

4.1.3 Controls for labor force participation

Based on the arguments in the subsection on the role of labor force participation in the theory section, we

try to control for labor force participation. If labor force participation is endogenized, it can be intuitively

seen that an increase in trade protection within the context of a Ricardian model should reduce labor market

participation rate, i.e., trade liberalization increase labor force participation. When we regress labor market

participation on either unweighted tariffs or import duties and include the same set of controls as in table

3, we obtain the following:

labor participation ratei = −0.496∗∗∗(0.125)

tariffi + controls

R2 = 0.39, N = 71

labor participation ratei = −0.506∗∗∗(0.210)

mdutyi + controls

R2 = 0.32, N = 67

where *** indicates significance at the 1% level. We obtain similar results with all the other protection

measures as well.

20 It may be argued that countries with good macroeconomic policies are also likely to be members of the GATT/WTO.

However, membership in the GATT/WTO has never been conditional on macro policies or on the level of unemployment.

19

As a further robustness check, table 5 adds two additional controls to the regressions of trade protection

on unemployment - one for labor market participation, and a second for female labor market participation.

Table 5 presents estimates with two direct measures of trade protection - tariffs (columns 1-3) and import

duties (columns 4-6). Columns 1 and 4 show OLS estimates while the rest present IV estimates. Columns 2

and 5 instrument only trade policy while columns 3 and 6 also instrument the labor participation variable.21

We use the crude death rate and HIV prevalence in the population aged 15-49, as additional instruments

in columns 3 and 6.22 Across measures of trade policy, and for both estimation techniques, we find that

countries who are more protectionist exhibit higher levels of unemployment. In fact, comparing the OLS

and IV coefficient estimates in table 5 to the ones in tables 3 and 4, we see that the magnitude of the

coefficients are quite similar. Total labor participation rate does not significantly impact the unemployment

rate in columns 1-3, where unweighted tariffs is the trade protection measure. For import duties, labor

market participation becomes insignificant in column 6, when we control for the potential endogeneity

in this measure. Finally, across all specifications, female labor market participation does not affect the

unemployment rate.23

4.2 Heckscher-Ohlin specification

The Heckscher-Ohlin prediction, summarized in proposition 2, is that an increase in trade restrictions will

raise unemployment in labor abundant countries and reduce them in capital-abundant countries. As a first

pass, we classified each country as capital or labor abundant according to whether its capital-labor ratio in

1990 was above or below the median capital-labor ratio. Next, we regressed unemployment on the trade

protection measures separately for the capital-abundant sample and for the labor-abundant sample. For

both samples, and for each of the direct measures of trade restrictions, we obtain a positive coefficient on

trade protection. However, subdividing the sample on the basis of the median is somewhat ad hoc. A

priori, we do not know the critical level of (K/L) where the relationship between trade restrictions and

unemployment changes sign. The following specification takes care of this problem by allowing the data to

tell us the exact location of this turning point:

21 In a recent paper, Lee and Wolpin (2006), who structurally estimate a two-sector labor-market equilibrium argue for

the inclusion of female labor participation, in such analyses. They point out, however, that the variation that is specific to

the participation rate of females is a function of fertility rates and the effect of children on female home time, which can be

considered fairly exogenous to our model (as well as to the Lee-Wolpin model).

22Data are from the World Development Indicators. Both variables depend overwhelmingly on the disease burden in a

particular country and are completely exogenous.

23Results with the GCR measure and for X+MGDP

are also robust to inclusion of the participation variables. The OTRI

measure fails to be significant. See online appendix.

20

Unemploymenti = α0 + α1TRi + α2TRi × (K/L)i + α3(K/L)i +Xiβ + i

where TRi is the extent of trade restrictions in country i, Unemployment i is the measure of unemployment,

(K/L)i is the capital-labor ratio for the year 1990 and Xi is a row vector of control variables. Taking the

partial derivative of Unempolymenti with respect to TRi, we have∂Unempolymenti

∂(TR)i= α1 + α2(K/L)i. The

prediction of the Proposition 2 is that α1 > 0 and α2 < 0 such that α1+α2(K/L)i ≷ 0 as (K/L)i ≶ (K/L)∗

where (K/L)∗ = − α1/α2 is the turning point capital-labor ratio determined endogenously from the data,

given our estimating equation. Another requirement for the prediction to hold is that (K/L)∗ should lie

within the range of values of (K/L) in the dataset, i.e., (K/L)MIN < (K/L)∗ < (K/L)MAX .

Table 6 presents the estimates with this specification. The last two rows count the number of countries

that are below (above) the critical capital labor ratio and have a positive (negative) relation between trade

restrictions and unemployment. As table 6 shows, apart from the import duty measure, we have very little

support for the Heckscher-Ohlin proposition. For the tariff measure α2 is positive rather than negative.

For GCR, α1 > 0 and significant, but α2 is not significant, which indicates a positive relationship between

trade barriers and unemployment. For OTRI, while the signs are correct, neither α1 nor α2 is significant.

For¡X+MGDP

¢the coefficients are insignificant and have the wrong sign. Even if we ignore the insignificance

of the coefficients, our estimates indicate that there is not a single country with a negative relationship

between trade restrictions and unemployment for the unweighted tariffs and GCR measures. For OTRI,

only 7 countries exhibit such a negative relationship. Finally, for the¡X+MGDP

¢measure, 41 out of 48 countries

exhibit a negative relation between trade volumes and unemployment.

Import duty is the only measure for which we observe some support for the Heckscher-Ohlin proposition.

Both α1 and α2 are significant and 16 out of the 47 countries exhibit a negative relationship between trade

protection and unemployment.24 However, even here the Heckscher-Ohlin prediction is not very robust.

When we instrument the trade protection measures (not shown), we fail to find support for the Heckscher-

Ohlin prediction, across all measures of protection, including import duty. Similarly, once we include

labor-market participation variables, α1 and α2 are insignificant across measures for trade protection (even

for import duties). This is true regardless of whether or not we instrument the trade policy and labor market

participation variables. These results, by no means, imply the absence of Heckscher-Ohlin type composition

effects. Rather we believe that such effects get dominated by Ricardian-type productivity-related effects of

trade. This interpretation of our empirical results seems most plausible to us.

24For import duty, the estimated critical capital-labor ratio equals 10.95 (in log terms) and 16 countries, whose capital-labor

ratios exceed this critical value, exhibit such a negative relationship.

21

5 Estimation with Panel Data

Our next objective is to examine how shifts in the degree of protection within a country affect the un-

employment rate. This task is non-trivial given that unemployment rates are subject to business cycle

fluctuations, longitudinal data on protection measures are sparse and trade policies tend to be very stable

over time. Nevertheless, we attempt to provide at least a partial view of the robustness of our results using

within-country variation. The use of yearly panel data also allows us to examine the differences between

the short-run and long-run effects of trade liberalization on unemployment.

For our panel results, we examine how unemployment responds in both the short and long run to a

permanent trade liberalization episode. The data on trade liberalization episode are from Wacziarg and

Welch (2008) who use a variety of primary and secondary data to provide liberalization dates for 110

countries. This data extends the original Sachs-Warner data on liberalization dates for the decade of the

1990s, with liberalization dates ranging from 1950 to 2001.25 Only permanent trade liberalization episodes

were considered. We operationalize our trade liberalization episode variable by coding a dummy that takes

the value one in the years following a permanent trade liberalization. Otherwise it takes the value 0. As

before, we include controls for GDP, working-age population, civil liberties and the two labor participation

variables.26 The data span 1985-2004 with summary statistics shown in table 1.

Our specification includes, a) the lagged unemployment rate as a regressor (lagged by one year); and

b) contemporaneous and lagged values of the liberalization dummy (lagged by 1-4 years). We estimate the

following dynamic specification:

uit = a1uit−1 + b0Lit + b1Lit−1 + ...+ b4Lit−4 +XitB+(ηi + υit) ; i = 1, 2, ...N ; t = 2, 4, ...T (28)

where uit is the unemployment rate in country i at time t, uit−1 is the lagged unemployment rate, Lit−k is

liberalization dummy in country i at time (t− k); Xit is the vector of control variables; ηi is a time invariant

country-specific effect possibly correlated with the explanatory variables and υit is an error term.27

Column (1) of table 7 presents the pooled OLS estimates of equation (28). Column (2) presents fixed-

effects estimates which eliminates the country-specific effect ηi by demeaning the variables. However, the

25The trade liberalization dates are obtained from a comprehensive survey of country case studies of liberalization. As such

they are not related to the Sachs-Warner dummy variable for openness, where the openness dummy is based on secondary

data on tariffs, non-tariff barriers, the black market premium on the exchange rate, state monopoly of exports, and a socialist

economic system. In a separate paper, Wacziarg and Wallack (2004) show that these liberalization dates are good indicators

of the timing of major trade policy changes and are associated with sustained increases in both imports and exports.

26The labor market institution variables and the black market premium are available for only a single year. Output volatility

by construction is cross-sectional since we calculate the volatility of output over time for each country.

27Measurement error in unemployment may be a concern with the presence of the lagged dependent variables on the right

hand side. However, if this error is driven by country-specific specific characteristics and varies little over time, it will be

subsumed within the country specific effect ηi.

22

within-estimates are likely to be inconsistent as well, since the transformed lagged dependent variable is

correlated with the transformed error term.28 Therefore, we employ a generalized method of moments

(GMM) procedure from Arellano and Bond (1991) to generate consistent estimates of the parameters of

interest. Estimation proceeds by first differencing the equation (28) - this eliminates the country-specific

effects ηi from the model - and instrumenting the lagged dependent variable by appropriately lagged levels

of the unemployment rate. We examine two sub-cases: In the first (column 3 of table 7), the liberalization

episode is assumed to be predetermined so that E [Litυis] = 0 for all s ≥ t and E [Litυis] 6= 0 for all s < t.

In the second (column 4 of table 7), the liberalization episode is assumed to be endogenous, E [Litυis] = 0

for all s > t and E [Litυis] 6= 0 for all s ≤ t. In both cases, appropriately lagged values of Lit, are available

as instruments. Finally, column 5 adds the labor market participation variables to column 4. For the other

explanatory variables, we adopt the weaker assumption that GDPit and labor participation variables are

predetermined, and that the working-age population and civil rights are exogenous.

Across specifications, we observe a very interesting pattern in the responsiveness of unemployment to

trade liberalization. The positive and significant coefficient on contemporaneous liberalization dummy indi-

cates a rise in unemployment in the year of liberalization. However, the negative and significant coefficients

on the same variable at lags 1 and 2 show that unemployment over a longer time lag experiences a reduction

due to trade liberalization. The dislocations caused by trade liberalization lead to an immediate short-term

spike in unemployment rates - a magnitude of increase is about 0.6% from column 3. Over a longer time

horizon of 2-3 years, employment recovers, and the rise in unemployment reverses. The liberalization episode

eventually leads to a long-run decline of 3.5% according to the coefficient estimates in column 3. In col-

umn 4, where we treat the liberalization dummy as endogenous, the estimates imply a 2.5% decline in

the unemployment rate in the long-run following a permanent trade liberalization. Finally, the last three

rows in table 7 report p−values from three specification tests suggested by Arellano and Bond (1991). The

tests indicate that the original error term vit, is serially uncorrelated, that the moment conditions are well

specified, and OID tests fail to reject the validity of our instruments.29

We also extended the panel results on trade liberalization to the Heckscher-Ohlin setting by interacting

the liberalization dummy with capital-labor ratio (please see online appendix at http://faculty.insead.edu/dutt/unemp).

Across specifications (pooled OLS, fixed-effects, Arellano-Bond GMM) neither the liberalization dummy

variable nor their interaction with capital-labor ratio are significant. In this sense, our panel results simply

28This correlation goes to zero only as T gets large. For our data T = 7 for the majority of countries, so this correlation

does not vanish.

29We also used the unweighted tariff rate for which we have the most comprehensive data over time. Similar to the results

with the liberalization dummy, we find that countries who initiated a decline in their overall unweighted tariffs experienced a

fall in their unemployment rates after a lag of three years. There is no evidence for a contemporaneous rise in the unemployment

rate. These results are available in the online appendix.

23

replicate our cross-sectional findings for the Heckscher-Ohlin specification.

6 Conclusions

In this paper, we present a model of trade and unemployment, in which unemployment is search induced

and trade arises as a result of Heckscher-Ohlin (H-O) comparative advantage based on differences in factor

proportions and/or Ricardian comparative advantage based on relative technological differences. We also

discuss comparative advantage arising out of intersectoral and international differences in search frictions,

efficiency and intensity. Our cross-sectional results provide fairly strong evidence for the steady-state pre-

diction arising when trade is primarily driven by Ricardian comparative advantage: protection increases

unemployment rates both across countries. This relationship is robust to controlling for employment laws,

trade union power, civil liberties, country and labor force size. Resolving endogeneity concerns through

the use of instrumental variables estimation leaves our results qualitatively unaffected. When we consider

permanent trade liberalization episodes, we observe an immediate rise in unemployment; in the longer-term

our estimates imply a reversal of this rise and an eventual decline in unemployment in the steady-state.

Overall, we find a striking difference in the short versus long-run responsiveness of unemployment to trade

liberalization. Finally, there is only weak and non-robust evidence for the Heckscher-Ohlin proposition

which states that the impact of trade policies on unemployment is conditional on whether the country

is labor abundant or capital abundant. We interpret this as the offsetting of the Heckscher-Ohlin type

composition effects by Ricardian-type productivity-related effects, rather than the complete absence of the

former.

24

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26

27

Table 1: Summary Statistics Variable Cross-Section Data Panel Data N Mean N Mean Unemployment Rate 90 9.87 1586 8.95 Unweighted Tariff 175 15.13 1290 13.37 Overall Trade Restrictiveness Index 89 16.13 GCR Trade Barriers 115 3.19 Import Duty 131 8.78 Openness: (X+M)/GDP 182 84.21 GDP 184 16.84 2065 18.09 Population (ages 15-64) 204 19.99 2484 15.48 Civil Liberties 186 3.67 2150 2.97 Employment laws index 83 0.43 Labor Union Power 83 0.49 Output Volatility 182 6.17 Black Market Premium 121 4.99 Capital-labor ratio 115 9.24 No. of years outside GATT/WTO 185 21.69 Domestic tax revenue share in total tax revenues 93 0.8 Labor market participation rate 199 69.22 2484 68.01 Female labor market participation rate 199 55.6 2484 54.18 Liberalization dummy 1998 0.73

28

ALBARGARM

AUSAUT

AZE

BEL

BGD

BGR

BHR

BHS

BLR

BLZ

BOLBRA

BRB

CAN

CHECHL

CHN

CIV

COL

CRICYP

CZE

DNK

DOM

DZA

ECU EGY

ESP

EST

ETH

FIN

FJI

FRA

GBR

GEOGRC

HKG

HUNIRL

ISRITA

JPN

KAZ

KOR

LCA

LKA

LTULUX

LVAMAC

MAR

MDAMEXMLT

MUSMYS

NIC

NLDNORNZL

PAN

PERPHLPOL

PRT PRYROMRUS

SGP

SLB

SLV

SURSVK SVN

SWE SYR

TTO TUN

TURUKR URY

USA

VENZAF

020

4060

Une

mpl

oym

ent R

ate

0 10 20 30 40 50Unweighted tariff

Figure 1: Tariffs and Unemployment Rate (1990s)

29

Table 2: The Effect of Trade Policies on the Unemployment Rate (Ricardian specification)

(1) (2) (3) (4) (5) Unweighted Tariff 0.287* (0.148) Overall Trade Restrictiveness Index 0.308*** (0.102) GCR trade barriers 1.124** (0.501) Import Duty 0.597*** (0.218) Openness: (X+M)/GDP -0.027** (0.014) Observations 87 53 77 82 89 R-squared 0.12 0.13 0.15 0.24 0.04

All regressions include a constant (not reported). Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1% All variables are averaged over the 1990s, except OTRI and GCR trade barriers which are available for a single year.

30

Table 3: The Effect of Trade Policies on the Unemployment Rate (Ricardian specification; with controls)

(1) (2) (3) (4) (5) Unweighted Tariff 0.351*** (0.098) Overall Trade Restrictiveness Index 0.179* (0.094) GCR trade barriers 1.402** (0.587) Import Duty 0.492*** (0.111) Openness: (X+M)/GDP -0.024* (0.013) Employment laws index -1.174 0.352 -2.035 -2.959 -2.788 (2.702) (2.752) (2.697) (2.599) (2.960) Labor Union Power 5.432* 0.742 4.961 7.602*** 5.924* (2.856) (3.797) (2.977) (2.785) (3.116) GDP 0.010 -1.603 -0.481 0.062 -0.772 (1.120) (1.880) (1.190) (1.157) (1.365) Population (ages 15-64) -0.732 1.037 -0.407 -0.459 -0.275 (1.219) (2.181) (1.301) (1.203) (1.492) Civil Liberties -1.052** -0.745 -0.754 -0.726 0.186 (0.405) (1.065) (0.451) (0.466) (0.672) Output Volatility 0.335 0.336 0.007 0.302 0.271 (0.302) (0.354) (0.319) (0.281) (0.335) Black Market Premium -0.015 0.013 -0.002 -0.028 -0.019 (0.028) (0.035) (0.025) (0.029) (0.033) Observations 55 35 55 54 55 R-squared 0.28 0.33 0.28 0.33 0.20

All regressions include a constant (not reported). Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1% All variables are averaged over the 1990s, except OTRI and GCR trade barriers which are available for a single year. Employment laws index and labor union power are available only for 1997.

31

Table 4: Instrumental Variables Results for the Effect of Trade Policies on Unemployment Rate (Ricardian specification; with controls)

(1) (2) (3) (4) (5) Unweighted Tariff 0.750** (0.345) Overall Trade Restrictiveness Index 0.101 (0.190) GCR trade barriers 1.994*** (0.560) Import Duty 0.664*** (0.196) Openness: (X+M)/GDP -0.065** (0.028) Employment laws index -2.331 -2.153 -4.438* -4.656* -4.599 (2.923) (3.276) (2.422) (2.402) (2.905) Labor Union Power 5.839 2.936 8.357** 8.847** 6.895** (4.257) (5.386) (3.450) (3.597) (2.995) GDP 1.682 -2.638 -0.437 0.095 1.255 (2.319) (2.470) (1.235) (1.647) (2.110) Population (ages 15-64) -2.165 2.467 -0.043 -0.501 -3.192 (2.619) (2.781) (1.389) (1.842) (2.658) Civil Liberties -1.645** -0.706 -0.935** -1.027** 1.417 (0.760) (1.631) (0.369) (0.438) (1.067) Output Volatility 0.284 0.607 0.333 0.151 0.405 (0.367) (0.506) (0.376) (0.348) (0.294) Black Market Premium -0.036 0.004 -0.036 -0.039 -0.055 (0.040) (0.056) (0.036) (0.033) (0.037) Observations 44 25 44 43 55 First stage partial R-squared 0.18 0.37 0.52 0.38 0.3 OID test p-value 0.84 0.01 0.9 0.76 0.13

All regressions include a constant (not reported). Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1% Instruments for trade policies: Share of tax revenues from domestic sources; Number of years the country stayed outside GATT since inception in 1948. Instruments for (X+M/GDP): Frankel-Romer instruments and remoteness index from Rose (2004). The last two rows report a partial R2 from the first stage regressions and the p-value from a test of overidentification.

32

Table 5: The Effect of Trade Policies on the Unemployment Rate (Ricardian specification; Controlling for Labor Market Participation)

(1) (2) (3) (4) (5) (6) Unweighted Tariff 0.267*** 0.686* 0.659* (0.089) (0.403) (0.360) Import duty 0.336** 0.500** 0.453** (0.154) (0.201) (0.204) Employment laws index -3.440 -3.345 -3.428 -4.999* -5.809** -5.638** (2.577) (2.774) (2.878) (2.588) (2.413) (2.518) Labor Union Power 6.125** 5.250 5.316 7.446*** 7.114** 7.073** (2.906) (3.888) (3.775) (2.709) (2.986) (2.935) GDP -0.428 1.487 1.330 -0.718 -0.388 -0.592 (1.019) (2.423) (2.227) (1.028) (1.356) (1.354) Population (ages 15-64) 0.092 -1.937 -1.770 0.645 -0.013 0.189 (1.115) (2.681) (2.495) (1.119) (1.452) (1.471) Civil Liberties -1.581*** -1.965** -1.946** -1.398** -2.037*** -2.077*** (0.520) (0.878) (0.891) (0.537) (0.548) (0.570) Output Volatility 0.342 0.229 0.230 0.286 0.126 0.148 (0.309) (0.380) (0.399) (0.296) (0.349) (0.381) Black Market Premium 0.010 -0.009 -0.007 0.007 0.006 0.005 (0.038) (0.041) (0.045) (0.039) (0.036) (0.041) Labor market participation rate -0.263 -0.295 -0.300 -0.323* -0.347** -0.287 (0.164) (0.189) (0.394) (0.179) (0.174) (0.337) Female labor market participation rate 0.010 0.083 0.082 0.041 0.016 -0.022 (0.120) (0.157) (0.252) (0.138) (0.136) (0.222) Observations 55 44 44 54 43 43 R-squared 0.41 0.46 First stage partial R-squared (trade policy)

0.15 0.17 0.35 0.38

First stage partial R-squared (participation rate))

0.32 0.32

OID test p-value 0.96 0.37 0.66 0.38 All variables are averaged over the 1990s. Employment laws index and labor union power are available only for 1997. All regressions include a constant (not reported). Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1% Instruments for trade policies: Share of tax revenues from domestic sources; Number of years the country stayed outside GATT since inception in 1948. Additional instruments when labor market participation is instrumented: Percentage of working age population; Crude death rate per 1000. Columns 1 and 4 present OLS estimates; columns 2, 3, 5 and 6 present IV estimates. In columns 2 and 5 we only instrument trade policies. In columns 3 and 6 we also instrument labor market participation. The last two rows report a partial R2 from the first stage regressions and the p-value from a test of overidentification.

33

Table 6: The Effect of Trade Policies on the Unemployment Rate (Hecksher-Ohlin specification; with controls)

Unweighted tariff

Overall Trade Restrictiveness Index

GCR Trade Barriers Import Duty Openness

(X+M/GDP)

Trade Policy Measure 0.227 0.958 9.230* 3.824** 0.158 (0.244) (0.856) (4.888) (1.443) (0.188) Trade Policy*Capital-labor ratio 0.015 -0.089 -0.763 -0.349** -0.017 (0.025) (0.094) (0.503) (0.149) (0.018) Capital-labor ratio 1.427 1.268 4.330* 4.521** 1.350 (1.911) (3.281) (2.538) (2.220) (2.364) Employment laws index -2.029 -1.126 -2.477 -3.208 -3.750 (2.667) (3.032) (2.725) (2.596) (2.940) Labor Union Power 6.346* 2.521 5.270 6.316* 7.768** (3.641) (6.177) (3.204) (3.375) (3.522) GDP -2.399 -2.639 -3.622* -4.478* -1.298 (2.273) (3.658) (2.103) (2.210) (2.210) Population (ages 15-64) 1.764 2.111 2.779 4.107* 0.181 (2.456) (3.914) (2.248) (2.328) (2.317) Civil Liberties -0.903* -0.904 -0.940* -0.874 0.224 (0.532) (1.002) (0.548) (0.596) (0.856) Output Volatility 0.179 0.632 0.325 0.329 0.472 (0.419) (0.532) (0.416) (0.347) (0.504) Black market premium -0.064 0.027 -0.035 -0.025 -0.037 (0.061) (0.070) (0.039) (0.042) (0.049) Observations 48 29 48 47 48 R-squared 0.31 0.42 0.37 0.42 0.27 Positive relation 48 22 48 31 7 Negative relation 0 7 0 16 41

Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1% All variables are averaged over the 1990s, except OTRI and GCR which are available for a single year. Employment laws index and labor union power are available only for 1997. The last 2 rows divides the number of observations into countries that have a positive and countries that have a negative relation between trade policy and unemployment rate.

34

Table 7: The Effect of Permanent Trade Liberalization on Unemployment Rate (Ricardian Specification)

(1) (2) (3) (4) (5) Unemployment (t – 1) 0.963*** 0.773*** 0.635*** 0.616*** 0.597*** (0.009) (0.053) (0.010) (0.007) (0.010) Liberalization dummy (t) 0.814** 0.701* 0.877*** 0.925*** 0.818*** (0.386) (0.385) (0.157) (0.146) (0.208) Liberalization dummy (t – 1) -0.841* -0.664* -1.983*** -1.549*** -1.346*** (0.429) (0.363) (0.179) (0.283) (0.211) Liberalization dummy (t – 2) -0.756* -0.653* -0.412** -0.481* -0.838** (0.429) (0.375) (0.194) (0.254) (0.335) Liberalization dummy (t – 3) 0.418 0.233 0.015 0.036 0.265 (0.325) (0.297) (0.177) (0.188) (0.218) Liberalization dummy (t – 4) -0.117 0.099 0.207 0.125 -0.206 (0.246) (0.270) (0.185) (0.212) (0.212) GDP (t) -0.196 -2.306*** -7.323*** -7.011*** -7.775*** (0.127) (0.709) (0.366) (0.332) (0.347) Population (ages 15-64) (t) 0.217 5.808*** 9.531*** 9.718*** 8.872*** (0.138) (1.641) (1.988) (1.477) (2.027) Civil liberties (t) -0.052 -0.132 -0.480*** -0.205*** -0.116*** (0.056) (0.151) (0.031) (0.038) (0.035) Labor market participation rate (t) -0.191 (0.120) Female labor market participation rate (t) 0.061 (0.081) Observations 1096 1096 1011 1011 1011 Number of countries 73 73 72 72 72 Specification test (p-values) OID test 1 1 1 Serial correlation: AR(1) test 0.09 0.09 0.08 Serial correlation: AR(2) test 0.14 0.14 0.13

Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1% Column 1 shows pooled OLS estimates; column 2 shows within-estimates with country-fixed effects; columns 3-5 present Arellano-Bond GMM (difference) estimates. In columns 3-5, all variables are in first-differences which removes country-fixed effects and the lagged differenced unemployment is instrumented by higher lags of the unemployment level. Column 3 treats the liberalization dummy as pre-determined and column 4 treats it as endogenous. Column 5 adds participation rates to column 4.